Most Recent Oracle 1Z0-1127-25 Exam Dumps

Prepare for the Oracle Cloud Infrastructure 2025 Generative AI Professional exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Oracle 1Z0-1127-25 exam and achieve success.

The questions for 1Z0-1127-25 were last updated on Mar 28, 2025.

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Question No. 1

Which is NOT a typical use case for LangSmith Evaluators?

AMeasuring coherence of generated text

BAligning code readability

CEvaluating factual accuracy of outputs

DDetecting bias or toxicity

Show Answer

Correct Answer: B

Comprehensive and Detailed In-Depth Explanation=

LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out---Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.

: OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.

Question No. 2

What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

ASupport for tokenizing longer sentences

BImproved retrievals for Retrieval Augmented Generation (RAG) systems

CEmphasis on syntactic clustering of word embeddings

DCapacity to translate text in over 100 languages

Show Answer

Correct Answer: B

Comprehensive and Detailed In-Depth Explanation=

Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus---embedding quality is. Option C (syntactic clustering) is too narrow---semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.

: OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.

Question No. 3

How does the structure of vector databases differ from traditional relational databases?

AA vector database stores data in a linear or tabular format.

BIt is not optimized for high-dimensional spaces.

CIt is based on distances and similarities in a vector space.

DIt uses simple row-based data storage.

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Correct Answer: C

Comprehensive and Detailed In-Depth Explanation=

Vector databases store data as high-dimensional vectors, optimized for similarity searches (e.g., cosine distance), unlike relational databases' tabular, row-column structure. This makes Option C correct. Option A and D describe relational databases. Option B is false---vector databases excel in high-dimensional spaces. Vector databases support semantic queries critical for LLMs.

: OCI 2025 Generative AI documentation likely contrasts these under data storage options.

Question No. 4

In which scenario is soft prompting especially appropriate compared to other training styles?

AWhen there is a significant amount of labeled, task-specific data available.

BWhen the model needs to be adapted to perform well in a different domain it was not originally trained on.

CWhen there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.

DWhen the model requires continued pre-training on unlabeled data.

Show Answer

Correct Answer: C

Comprehensive and Detailed In-Depth Explanation=

Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.

: OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

Question No. 5

In the simplified workflow for managing and querying vector data, what is the role of indexing?

ATo convert vectors into a non-indexed format for easier retrieval

BTo map vectors to a data structure for faster searching, enabling efficient retrieval

CTo compress vector data for minimized storage usage

DTo categorize vectors based on their originating data type (text, images, audio)

Show Answer

Correct Answer: B

Comprehensive and Detailed In-Depth Explanation=

Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards---indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose---it's about search efficiency. Indexing powers scalable vector queries.

: OCI 2025 Generative AI documentation likely explains indexing under vector database operations.

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